{"library":"mapie","title":"MAPIE (Mapping Prediction Intervals)","description":"MAPIE (Mapping Prediction Intervals) is a scikit-learn-compatible Python library for estimating prediction intervals. It provides tools for both regression and classification tasks, leveraging conformal prediction methods to quantify uncertainty. The current version is 1.3.0, and the library maintains an active release cadence with several major and minor updates per year.","language":"python","status":"active","last_verified":"Thu Apr 16","install":{"commands":["pip install mapie"],"cli":null},"imports":["from mapie.regression import MapieRegressor","from mapie.classification import MapieClassifier","from mapie.time_series_regression import MapieTimeSeriesRegressor","from mapie.risk_control import BinaryClassificationController"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_regression\nfrom mapie.regression import MapieRegressor\n\n# 1. Generate synthetic data\nX, y = make_regression(n_samples=500, n_features=1, noise=20, random_state=42)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.2, random_state=42\n)\n\n# 2. Fit a MAPIE regressor\nregressor = LinearRegression()\nmapie_regressor = MapieRegressor(regressor, cv=\"split\", random_state=42)\nmapie_regressor.fit(X_train, y_train)\n\n# 3. Predict prediction intervals\ny_pred, y_pis = mapie_regressor.predict(X_test, alpha=0.1)\n\n# 4. Print results (example)\nprint(f\"Predicted value for first test sample: {y_pred[0]:.2f}\")\nprint(f\"Prediction interval for first test sample: [{y_pis[0, 0, 0]:.2f}, {y_pis[0, 1, 0]:.2f}]\")","lang":"python","description":"This quickstart demonstrates how to use `MapieRegressor` to train a model and predict prediction intervals on synthetic regression data. It uses a `LinearRegression` model as the base estimator.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}